Facial recognition is quickly becoming a disruptive technology with few limits imposed by privacy policy. Academic researchers, however, have found ways to -- at least temporarily -- cause problems for certain classes of facial-recognition algorithms, taking advantages of weaknesses in the training algorithm or the resultant recognition model. Last week, a team of computer-science researchers at the National University of Singapore (NUS) published a technique that locates the areas of an image where changes can best disrupt image-recognition algorithms, but where those changes are least noticeable to humans. The technique is general in that it can be used to develop an attack against other machine-learning (ML) algorithms, but the researchers only developed a specific instance, says Mohan Kankanhalli, a professor in the NUS Department of Computer Science and co-author of a paper on the adversarial attack. "Currently, we need to know the class [of algorithm] and can develop a solution for that," he says.

Artificial intelligence (AI) is designed to put us out of the picture. Still, we shouldn't fret since a recently published study discovered how humans can be on top of things in systems that rely on AI. The study, carried out by researchers at the Ecole Polytechnique Fédérale de Lausanne (EPFL), explained that while AI will always find ways to bypass human intervention and build an independent solution, operators must look for ways to keep themselves above the machines and prevent them from circumventing human command. The solution that the researchers found was to change the rules midstream: To borrow a psychological term, instead of punishing AI for learning the process and gaining independence, operators opined that they be one step ahead of the machines and keep leading them by moving the proverbial carrot. In AI, machines are programmed to learn from their tasks -- the do an activity, observe what happens, adapt their behavior, and apply it to the next action.

March 2, 2017 --Computer engineers have created some amazingly small devices, capable of storing entire libraries of music and movies in the palm of your hand. But geneticists say Mother Nature can do even better. DNA, where all of biology's information is stored, is incredibly dense. The whole genome of an organism fits into a cell that is invisible to the naked eye. That's why computer scientists are turning to microbiology to design the next best way to store humanity's ever-increasing collection of digital data.

Over the last decade, computer scientists have harnessed crowds of Internet users to solve tasks that are notoriously difficult to crack with computers alone, such as determining whether an image contains a tree, rating the relevance of websites, and verifying phone numbers. The machine learning community was early to embrace so-called crowdsourcing to quickly and inexpensively obtain the vast quantities of labeled data needed to train machine learning systems how to classify images or recognize speech, for example. Labeled data are essentially sets of teaching examples, such as pictures of cats that are tagged with the word "cat." Usually this handoff of labeled data is where interaction with the crowd ends. Are there better ways to make use of the crowd?

When Robert Schapire started studying theoretical machine learning in graduate school three decades ago, the field was so obscure that what is today a major international conference was just a tiny workshop, so small that even graduate students were routinely excluded. But it has become one of the hottest fields in computer science, turning once-obscure academic gatherings like the upcoming Annual Conference on Neural Information Processing Systems in Barcelona, Spain, into a sold-out affair attended by thousands of computer scientists from top corporations and academic institutions. "It's been really something to see this field develop, and to see things that seemed impossible become possible in my lifetime," said Schapire, a principal researcher in Microsoft's New York City research lab whose machine learning research is widely used in the field. The NIPS conference, which starts Monday, is so popular because machine learning has quickly become an indispensable tool for developing technology that consumers and businesses want, need and love. Machine learning is the basis for technology that can translate speech in real time, help doctors read radiology scans and even recognize emotions on people's faces.

When Robert Schapire started studying theoretical machine learning in graduate school three decades ago, the field was so obscure that what is today a major international conference was just a tiny workshop, so small that even graduate students were routinely excluded. But it has become one of the hottest fields in computer science, turning once-obscure academic gatherings like the upcoming Annual Conference on Neural Information Processing Systems in Barcelona, Spain, into a sold-out affair attended by thousands of computer scientists from top corporations and academic institutions. "It's been really something to see this field develop, and to see things that seemed impossible become possible in my lifetime," said Schapire, a principal researcher in Microsoft's New York City research lab whose machine learning research is widely used in the field. The NIPS conference, which starts Monday, is so popular because machine learning has quickly become an indispensable tool for developing technology that consumers and businesses want, need and love. Machine learning is the basis for technology that can translate speech in real time, help doctors read radiology scans and even recognize emotions on people's faces.

This month, I will attend the Conference and Workshop on Neural Information Processing Systems (NIPS), the premier gathering in the machine learning field. I've participated in this conference most years since it began in 1987 and I'm looking forward once again to catching up with colleagues and friends as well as exploring new developments in the field. Until recently, the conference attracted a few hundred attendees. The number of participants has grown rapidly in recent years and this year there are more than 4,500 people registered! This explosion of activity in machine learning is remarkable and reflects the positive trend of research making its way to the marketplace.

When Robert Schapire started studying theoretical machine learning in graduate school three decades ago, the field was so obscure that what is today a major international conference was just a tiny workshop, so small that even graduate students were routinely excluded. But it has become one of the hottest fields in computer science, turning once-obscure academic gatherings like the upcoming Annual Conference on Neural Information Processing Systems in Barcelona, Spain, into a sold-out affair attended by thousands of computer scientists from top corporations and academic institutions. "It's been really something to see this field develop, and to see things that seemed impossible become possible in my lifetime," said Schapire, a principal researcher in Microsoft's New York City research lab whose machine learning research is widely used in the field. The NIPS conference, which starts Monday, is so popular because machine learning has quickly become an indispensable tool for developing technology that consumers and businesses want, need and love. Machine learning is the basis for technology that can translate speech in real time, help doctors read radiology scans and even recognize emotions on people's faces.